The MNIST data set [1]} is a set of 60000 grayscale images with size \(28\times 28\)  pixels that contains hand drawings of digits from zero to nine. Figure REF  shows the reconstruction of images by the features and with the generator applied to them, compared with traditional AE, VAE and GAN. While the features tend to be noisy, the generator can sharpen the images to resemble better the original inputs. Figure REF  shows random samples from the generator with a class layer coupled to the encoder. The same image also shows the interpolation of the latent vector between four pairs of images to demonstrate the continuity of transitions on the latent space.
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